def prepare_data(self, images, labels=None):
if (labels is None):
for (i, image) in enumerate(images):
number = ("%0.3d" % (i+1))
path_save = path.join(self.dn_test_out, mkdir=True)
image, _ = dip.preprocessor(image, None)
original_name = (const.fn_PREPROCESSING % (number))
data.imwrite(path.join(path_save, original_name), image)
yield self.arch.prepare_input(image)
else:
for (image, label) in zip(images, labels):
(image, label) = dip.preprocessor(image, label)
yield self.arch.prepare_input(image), self.arch.prepare_input(label)
def fetch_from_paths(images, labels):
image_list, label_list = [], []
for img, lab in zip(images, labels):
img_fetch = sorted(glob(path.join(img, const.FILTER)))
lab_fetch = sorted(glob(path.join(lab, const.FILTER)))
img_fetch, lab_fetch = misc.shuffle(img_fetch, lab_fetch)
for i, l in zip(img_fetch, lab_fetch):
image_list.append(i)
label_list.append(l)
image = np.array([cv2.imread(item, 1) for item in image_list])
label = np.array([cv2.imread(item, 1) for item in label_list])
return image, label
def update(self):
from os import walk
found = []
for directory in self.settings.directories():
if path.exists(directory):
for root, dirnames, filenames in walk(directory):
for filename in filenames:
# Get around the 256 character limit by prepending \\\\?\\
absolute = path.join("\\\?\\", root, filename)
if filename.lower().endswith(tuple(self.settings.file_extensions)):
mode, ino, dev, nlink, uid, gid, size, atime, mtime, ctime = stat(absolute)
if size > self.settings.minimum_size_byte:
found.append(Film(abspath=absolute, last_modification=get_datetime(mtime), size=size))
else:
self.settings.directories().remove(directory)
# Film has been removed from disk - remove from library
for film in self.films:
if film not in found:
#print('Removed: {}'.format(film.print()))
self.films.remove(film)
#del film
# New film - add to library
for film in found:
if film not in self.films:
#print('Found: {}'.format(film.print()))
self.films.append(film)
def tolabel():
dn_tolabel = path.out(const.dn_TOLABEL, mkdir=False)
if path.exist(dn_tolabel):
dir_save = path.out(const.dn_TOLABEL)
images = data.fetch_from_path(dir_save)
for (i, image) in enumerate(images):
path_save = path.join(dir_save, "label", mkdir=True)
file_name = ("%0.3d.png" % (i+1))
file_save = path.join(path_save, file_name)
img_pp, _ = dip.preprocessor(image, None)
data.imwrite(file_save, img_pp)
else:
print("\n>> Folder not found (%s)\n" % dn_tolabel)
def save_predict(self, original, image):
path_save = path.join(self.dn_test_out, mkdir=True)
with open(path.join(path_save, (const.fn_SEGMENTATION)), 'w+') as f:
for (i, image) in enumerate(image):
number = ("%0.3d" % (i+1))
image_name = (const.fn_PREDICT % (number))
image = dip.posprocessor(original[i], self.arch.prepare_output(image))
data.imwrite(path.join(path_save, image_name), image)
seg = (image == 255).sum()
f.write(("Image %s was approximately %f segmented (%s pixels)\n" % (number, (seg/image.size), seg)))
original_name = (const.fn_ORIGINAL % (number))
data.imwrite(path.join(path_save, original_name), original[i])
overlay_name = (const.fn_OVERLAY % (number))
overlay = dip.overlay(original[i], image)
data.imwrite(path.join(path_save, overlay_name), overlay)
f.close()
def fetch_from_path(images):
image_list = sorted(glob(path.join(images, const.FILTER)))
image = np.array([cv2.imread(item, 1) for item in image_list])
return image
def length_from_path(file_dir, *dirs):
length_fetch = len(glob(path.join(file_dir, const.FILTER)))
for x in dirs:
length_fetch += len(glob(path.join(x, const.FILTER)))
return length_fetch